Embodiments relate to an RFID reader. Further embodiments relate to a method for adjusting a frame length of an RFID system comprising an RFID reader and at least two RFID tags. Some embodiments relate to a new frame length optimization using collision recovery probability for frame slotted ALOHA (ALOHA is a channel access method for wireless transmission that allows uncoordinated users to share a common transmission resource).
In the recent years, the number of applications that use Radio Frequency Identification (RFID) Systems has increased, and the identification rate became one of the most critical issues in these applications. Such RFID networks consist of: 1) Readers (Interrogators), which are responsible of scanning the interrogation area and identifying the tags. 2) Tags (Transponders), which store the data to be read by the readers. In RFID systems, the tags typically share a common communications channel. Thus, there is a certain probability of tag-collisions, i.e. multiple tags answers simultaneously. This collision probability naturally increases in dense networks with many tags. As a result, the reader is responsible for coordinating the network and has to avoid tags collisions using specific anti-collision algorithms. This application focuses mainly on Ultra High Frequency tags which follow EPCglobal Class-1 Gen-2 standards [EPC radio-frequency protocols class-1 generation-2 UHF RFID protocol for communications at 860 MHz 960 MHz version 1.1.0 2006].
According to EPCglobal Class-1 Gen-2 standards, the conventional anti-collision algorithm is Frame Slotted ALOHA (FSA) algorithm, which is only Medium Access Control (MAC) layer protocol. In this algorithm, only the single tag reply (successful slot) are able to be decoded and then identified. Therefore, the conventional definition of the expected reading efficiency ηconv is equivalent to the probability of success P(S) [H. Vogt, “Efficient object identification with passive RFID rags”, in International Conference on Pervasive Computing, Zurich, August 2002]:ηconv=P(S)=P(1)where
            (      1      )        =                  n        L            ⁢                        (                      1            -                          1              L                                )                          n          -          1                      ,n represents the number of tags in the reading area, and L is the frame length. The main goal is to find the optimal frame length L, which maximizes the reading efficiency ηconv. Based on the above equation, the reading efficiency ηconv is maximized to be ηconv(max)=36% when L=n as shown in [H. Vogt, “Efficient object identification with passive RFID rags”, in International Conference on Pervasive Computing, Zürich, August 2002].
However, in recent years some research groups concentrated more on the physical layer (PHY): Khasgiwale et al, [R. U. A. R. S. Khasgiwale and D. W. Engels, “Extracting information from tag collisions”, IEEE International Conference RFID, 2009] could extract information from the physical layer about the number of collided tags at the collided slot, which helps a lot in the estimation of the exact number of tags in the reading area. Shen et al. [D. P. R. A. B. L. D. Shen, G. Woo and J. Wang, “Separation of multiple passive RFID signals using software defined radio”, IEEE International Conference RFID, 2009] proposed a collision recovery algorithm for the collided tags based on the signal constellations, however he focused only on Low Frequency (LF) Tags. Christoph Angerer [C. Angerer, R. Langwieser, and M. Rupp, “RFID reader receivers for physical layer collision recovery”, Communications, IEEE Transactions on, vol. 58, pp. 3526_3537, December 2010] focused on the collision recovery of UHF tags. The authors used the characteristics of the RFID signals to separate signals from collisions at the physical layer. However, due to the limitation of the channel estimation, the authors proposed a collision recovery only for two tags. They also have proposed a new reading efficiency metric which includes the tags which are recovered based on the PHY layer work. This reading efficiency can be expressed as:ηPHY=P(1)+P(2)where
      P    ⁡          (      2      )        =            n      2        ⁢                  (                  1          L                )            2        ⁢                            (                      1            -                          1              L                                )                          n          -          2                    .      Based on the above equation, the authors assumed that the reader will resolve 1 from the two tags collided slots, and then they proposed a fixed frame length which maximize the efficiency in the above equation which is L=0.707*n.